论文题目：Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles
论文：https://arxiv.org/abs/2206.02424
代码：https://github.com/AlanLi1997/Slim-neck-by-GSConv
直接步入正题~~~
目标：为YOLOv5模型构建一个简单高效的Neck模块。考虑了卷积方法、特征融合结构、计算效率、计算成本效益等诸多因素。
一、GSConv
class GSConv(nn.Module): # GSConv https://github.com/AlanLi1997/slim-neck-by-gsconv def __init__(self, c1, c2, k=1, s=1, g=1, act=True): super().__init__() c_ = c2 // 2 self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): x1 = self.cv1(x) x2 = torch.cat((x1, self.cv2(x1)), 1) # shuffle b, n, h, w = x2.data.size() b_n = b * n // 2 y = x2.reshape(b_n, 2, h * w) y = y.permute(1, 0, 2) y = y.reshape(2, -1, n // 2, h, w) return torch.cat((y[0], y[1]), 1)将YOLOv5s.yaml的Neck模块中的Conv换成GSConv
1、将GSConv代码加入common.py文件中
2、找到yolo.py文件里的parse_model函数，将类名加入进去
3、修改配置文件，将YOLOv5s.yaml的Neck模块中的Conv换成GSConv
~~~此处有一个疑问，官方给出的GSConv代码中为什么没用DWConv呢？希望知道的朋友在评论区指点一下~~~
二、GSConv+Slim Neck
1、GSBottleneck
class GSBottleneck(nn.Module): # GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv def __init__(self, c1, c2, k=3, s=1): super().__init__() c_ = c2 // 2 # for lighting self.conv_lighting = nn.Sequential( GSConv(c1, c_, 1, 1), GSConv(c_, c2, 1, 1, act=False)) # for receptive field self.conv = nn.Sequential( GSConv(c1, c_, 3, 1), GSConv(c_, c2, 3, 1, act=False)) self.shortcut = Conv(c1, c2, 3, 1, act=False) def forward(self, x): return self.conv_lighting(x) + self.shortcut(x)2、VoVGSCSP
class VoVGSCSP(nn.Module): # VoV-GSCSP https://github.com/AlanLi1997/slim-neck-by-gsconv def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(2 * c_, c2, 1) self.m = nn.Sequential(*(GSBottleneck(c_, c_) for _ in range(n))) def forward(self, x): x1 = self.cv1(x) return self.cv2(torch.cat((self.m(x1), x1), dim=1))将YOLOv5s.yaml的Neck模块中的Conv换成GSConv，C3模块换为VoVGSCSP模块
1、将以下代码加入common.py文件中
class GSConv(nn.Module): # GSConv https://github.com/AlanLi1997/slim-neck-by-gsconv def __init__(self, c1, c2, k=1, s=1, g=1, act=True): super().__init__() c_ = c2 // 2 self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): x1 = self.cv1(x) x2 = torch.cat((x1, self.cv2(x1)), 1) # shuffle b, n, h, w = x2.data.size() b_n = b * n // 2 y = x2.reshape(b_n, 2, h * w) y = y.permute(1, 0, 2) y = y.reshape(2, -1, n // 2, h, w) return torch.cat((y[0], y[1]), 1)class GSBottleneck(nn.Module): # GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv def __init__(self, c1, c2, k=3, s=1): super().__init__() c_ = c2 // 2 # for lighting self.conv_lighting = nn.Sequential( GSConv(c1, c_, 1, 1), GSConv(c_, c2, 1, 1, act=False)) # for receptive field self.conv = nn.Sequential( GSConv(c1, c_, 3, 1), GSConv(c_, c2, 3, 1, act=False)) self.shortcut = nn.Identity() def forward(self, x): return self.conv_lighting(x)class VoVGSCSP(nn.Module): # VoV-GSCSP https://github.com/AlanLi1997/slim-neck-by-gsconv def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(2 * c_, c2, 1) self.m = nn.Sequential(*(GSBottleneck(c_, c_) for _ in range(n))) def forward(self, x): x1 = self.cv1(x) return self.cv2(torch.cat((self.m(x1), x1), dim=1))2、找到yolo.py文件里的parse_model函数，将类名加入进去，注意有两处需要添加的地方
3、修改配置文件，将YOLOv5s.yaml的Neck模块中的Conv换成GSConv ，C3模块换为VoVGSCSP
Appendix
下图是原论文中给出的结构图，个人对照源码后觉得这里多画了一个GSConv模块(红色框里所示)，如果有知道的大佬望在评论区指点一下。
